Abstract

The paper presents two approaches for sensor fault detection and discrimination within a group, in real-time. In the first approach, the concept of y-indices is proposed through use of a transpose formulation of the data matrices traditionally used in Principal Component Analysis (PCA). The proposed formulation is introduced to measure the differences between sensor reading datasets in the ‘sensor domain’. This addresses problems associated with traditional PCA methods when measurement data is subject to bias and drifting, as is characteristic of transient power demand or load changes, for instance, which can lead to excessive false alarms. In the second approach, Residual Errors (REs) are generated from the residual sub-space in PCA, and used to detect abnormal operating conditions, and Faulty Sensor Identification Indices (FSIIs) are introduced to classify the type of fault (sensor or component) and identify which sensor is in error. The methods are applied in a time rolling window on experimental in-field data during the commissioning of an industrial 15MW turbine. It is shown that in-operation sensor faults can be detected through use of both y-indices, and REs and FSIIs, and sensor faults and component faults can be discriminated.